{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,18]],"date-time":"2026-03-18T02:56:20Z","timestamp":1773802580326,"version":"3.50.1"},"reference-count":0,"publisher":"Association for the Advancement of Artificial Intelligence (AAAI)","issue":"18","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["AAAI"],"abstract":"<jats:p>Humanoid robots exhibit significant potential in executing diverse human-level skills. However, current research predominantly relies on data-driven approaches that necessitate extensive training datasets to achieve robust multimodal decision-making capabilities and generalizable visuomotor control. These methods raise concerns due to the neglect of geometric reasoning in unseen scenarios and the inefficient modeling of robot-target relationships within the training data, resulting in a significant waste of training resources. To address these limitations, we present the Recurrent Geometric-prior Multimodal Policy (RGMP), an end-to-end framework that unifies geometric-semantic skill reasoning with data-efficient visuomotor control. For perception capabilities, we propose the Geometric-prior Skill Selector, which infuses geometric inductive biases into a vision language model, producing adaptive skill sequences for unseen scenes with minimal spatial common sense tuning. To achieve data-efficient robotic motion synthesis, we introduce the Adaptive Recursive Gaussian Network, which parameterizes robot-object interactions as a compact hierarchy of Gaussian processes that recursively encode multi-scale spatial relationships, yielding dexterous, data-efficient motion synthesis even from sparse demonstrations. Evaluated on both our humanoid robot and desktop robot, the RGMP framework achieves 87% task success in generalization tests and exhibits 5\u00d7 greater data efficiency than the state-of-the-art model. This performance underscores its superior cross-domain generalization, paving the way for more versatile and data-efficient robotic systems.<\/jats:p>","DOI":"10.1609\/aaai.v40i18.38539","type":"journal-article","created":{"date-parts":[[2026,3,18]],"date-time":"2026-03-18T00:37:54Z","timestamp":1773794274000},"page":"15153-15161","source":"Crossref","is-referenced-by-count":0,"title":["RGMP: Recurrent Geometric-prior Multimodal Policy for Generalizable Humanoid Robot Manipulation"],"prefix":"10.1609","volume":"40","author":[{"given":"Xuetao","family":"Li","sequence":"first","affiliation":[]},{"given":"Wenke","family":"Huang","sequence":"additional","affiliation":[]},{"given":"Nengyuan","family":"Pan","sequence":"additional","affiliation":[]},{"given":"Kaiyan","family":"Zhao","sequence":"additional","affiliation":[]},{"given":"Songhua","family":"Yang","sequence":"additional","affiliation":[]},{"given":"Yiming","family":"Wang","sequence":"additional","affiliation":[]},{"given":"Mengde","family":"Li","sequence":"additional","affiliation":[]},{"given":"Mang","family":"Ye","sequence":"additional","affiliation":[]},{"given":"Jifeng","family":"Xuan","sequence":"additional","affiliation":[]},{"given":"Miao","family":"Li","sequence":"additional","affiliation":[]}],"member":"9382","published-online":{"date-parts":[[2026,3,14]]},"container-title":["Proceedings of the AAAI Conference on Artificial Intelligence"],"original-title":[],"link":[{"URL":"https:\/\/ojs.aaai.org\/index.php\/AAAI\/article\/download\/38539\/42501","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/ojs.aaai.org\/index.php\/AAAI\/article\/download\/38539\/42501","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,3,18]],"date-time":"2026-03-18T00:37:54Z","timestamp":1773794274000},"score":1,"resource":{"primary":{"URL":"https:\/\/ojs.aaai.org\/index.php\/AAAI\/article\/view\/38539"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,3,14]]},"references-count":0,"journal-issue":{"issue":"18","published-online":{"date-parts":[[2026,3,17]]}},"URL":"https:\/\/doi.org\/10.1609\/aaai.v40i18.38539","relation":{},"ISSN":["2374-3468","2159-5399"],"issn-type":[{"value":"2374-3468","type":"electronic"},{"value":"2159-5399","type":"print"}],"subject":[],"published":{"date-parts":[[2026,3,14]]}}}